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Showing 161 - 180 results of 2,751 for search '(((( algorithms lung function ) OR ( algorithm could function ))) OR ( algorithm python function ))', query time: 0.49s Refine Results
  1. 161
    Manhattan plot of conditional analyses.

    Manhattan plot of conditional analyses. by Ka Wo Wong (18625847)

    Published 2024
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  2. 162
    Number of synonymous and non-synonymous variants in the EBV genome.

    Number of synonymous and non-synonymous variants in the EBV genome. by Ka Wo Wong (18625847)

    Published 2024
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  3. 163
    Phylogenetic and population structure analysis.

    Phylogenetic and population structure analysis. by Ka Wo Wong (18625847)

    Published 2024
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  4. 164
    Meta-analysis of EBV GWAS of NPC.

    Meta-analysis of EBV GWAS of NPC. by Ka Wo Wong (18625847)

    Published 2024
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  5. 165
    Characteristics of samples in this study.

    Characteristics of samples in this study. by Ka Wo Wong (18625847)

    Published 2024
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  6. 166
    SNP profile characterizing high risk EBV lineage.

    SNP profile characterizing high risk EBV lineage. by Ka Wo Wong (18625847)

    Published 2024
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  7. 167
    Haplotype analysis of 56 variants that passed p-value cut-off in the meta-analysis.

    Haplotype analysis of 56 variants that passed p-value cut-off in the meta-analysis. by Ka Wo Wong (18625847)

    Published 2024
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  8. 168
    Comparative analysis of EBV genomes in South China.

    Comparative analysis of EBV genomes in South China. by Ka Wo Wong (18625847)

    Published 2024
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  9. 169
    Characteristic chart of algorithm efficiency.

    Characteristic chart of algorithm efficiency. by Wang Xiaoqian (10502222)

    Published 2025
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  10. 170
    A comparison between the static Python-based visualizations of the p65 activity in activated fibroblasts and the dynamic, HTML-based visualizations that use these same reduction methods.

    A comparison between the static Python-based visualizations of the p65 activity in activated fibroblasts and the dynamic, HTML-based visualizations that use these same reduction me... by Hector Torres (11708207)

    Published 2025
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  11. 171
    Table3_Identification of Novel Lung Cancer Driver Genes Connecting Different Omics Levels With a Heat Diffusion Algorithm.XLSX

    Table3_Identification of Novel Lung Cancer Driver Genes Connecting Different Omics Levels With a Heat Diffusion Algorithm.XLSX by Fei Yuan (24924)

    Published 2022
    “…In this study, a computational investigation was conducted on lung cancer driver genes. Four omics levels, namely, epigenomics, genomics, transcriptomics, and post-transcriptomics, were involved. …”
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  12. 172
    Table4_Identification of Novel Lung Cancer Driver Genes Connecting Different Omics Levels With a Heat Diffusion Algorithm.XLSX

    Table4_Identification of Novel Lung Cancer Driver Genes Connecting Different Omics Levels With a Heat Diffusion Algorithm.XLSX by Fei Yuan (24924)

    Published 2022
    “…In this study, a computational investigation was conducted on lung cancer driver genes. Four omics levels, namely, epigenomics, genomics, transcriptomics, and post-transcriptomics, were involved. …”
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  13. 173
    Table2_Identification of Novel Lung Cancer Driver Genes Connecting Different Omics Levels With a Heat Diffusion Algorithm.XLSX

    Table2_Identification of Novel Lung Cancer Driver Genes Connecting Different Omics Levels With a Heat Diffusion Algorithm.XLSX by Fei Yuan (24924)

    Published 2022
    “…In this study, a computational investigation was conducted on lung cancer driver genes. Four omics levels, namely, epigenomics, genomics, transcriptomics, and post-transcriptomics, were involved. …”
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  14. 174
    Table1_Identification of Novel Lung Cancer Driver Genes Connecting Different Omics Levels With a Heat Diffusion Algorithm.XLSX

    Table1_Identification of Novel Lung Cancer Driver Genes Connecting Different Omics Levels With a Heat Diffusion Algorithm.XLSX by Fei Yuan (24924)

    Published 2022
    “…In this study, a computational investigation was conducted on lung cancer driver genes. Four omics levels, namely, epigenomics, genomics, transcriptomics, and post-transcriptomics, were involved. …”
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  15. 175
    Overview of functionality supported by PyNoetic and its various modules, including the live analysis and programmable flowchart.

    Overview of functionality supported by PyNoetic and its various modules, including the live analysis and programmable flowchart. by Gursimran Singh (575288)

    Published 2025
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  16. 176
    Cost functions implemented in Neuroptimus.

    Cost functions implemented in Neuroptimus. by Máté Mohácsi (20469514)

    Published 2024
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  17. 177
    Descriptive statistics of the dataset with mean, standard deviation (SD), median, and the lower (quantile 5%) and upper (quantile 95%) boundary of the 90% confidence interval.

    Descriptive statistics of the dataset with mean, standard deviation (SD), median, and the lower (quantile 5%) and upper (quantile 95%) boundary of the 90% confidence interval. by Achim Langenbucher (3156741)

    Published 2022
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  18. 178
    Optimised formula constants for the SRKT, the Hoffer Q, the Holladay 1, Haigis (with optimised a0 and preset values a1 = 0.4 / a2 = 0.1, Haigis1; and with optimised a0 / a1 / a2 constant triplet, Haigis3), and Castrop formula.

    Optimised formula constants for the SRKT, the Hoffer Q, the Holladay 1, Haigis (with optimised a0 and preset values a1 = 0.4 / a2 = 0.1, Haigis1; and with optimised a0 / a1 / a2 co... by Achim Langenbucher (3156741)

    Published 2022
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  19. 179
    Cumulative density function (CDF) of prediction error (PE, achieved spherical equivalent–formula predicted spherical equivalent) for the SRKT, Hoffer Q, Holladay1, Haigis (with optimised a0 and preset values a1 = 0.4 / a2 = 0.1 (Haigis1); and with optimised a0 / a1 / a2 constant triplet (Haigis3)), and Castrop formula.

    Cumulative density function (CDF) of prediction error (PE, achieved spherical equivalent–formula predicted spherical equivalent) for the SRKT, Hoffer Q, Holladay1, Haigis (with opt... by Achim Langenbucher (3156741)

    Published 2022
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  20. 180
    Prediction error (PE) as the difference between achieved and formula predicted spherical equivalent for 8 different statistical metrics of formula constant optimisation and various formulae under test.

    Prediction error (PE) as the difference between achieved and formula predicted spherical equivalent for 8 different statistical metrics of formula constant optimisation and various... by Achim Langenbucher (3156741)

    Published 2022
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